The following is a summary of the article Time-Varying Evaluation of Compound Drought and Hot Extremes in Machine Learning–Predicted Ensemble CMIP5 Future Climate: A Multivariate Multi-Index Approach, authored by Sushree Swagatika Swain, Aff.M.ASCE, Ashok Mishra, and Chandranath Chatterjee, and published in the Journal of Hydrologic Engineering, Volume 29, Issue 2, April 2024.
Compound extremes can manifest through the joint distribution or dynamic interplay of multiple variables, indicating significant interdependence among various extremes, which profoundly influence numerous industries. Addressing these compound extremes within spatial and temporal domains remains a formidable challenge for climate scientists. Forecasting these phenomena in future climatic contexts is more complex, given the plethora of globally accepted climate models (GCMs) and regional climate models (RCMs). Consequently, this study proposes a Compound Extreme Index (CEI), integrating precipitation, temperature, and runoff to enhance sustainable water resource management. This research introduces a novel perspective on modeling frameworks for compound drought and heat extremes (CDHE) by developing the Standardized Compound Extreme Event Index (SCEEI), emphasizing trend and prediction analyses and severity evaluations over time. “The discharge data used in this study are available from the corresponding author upon reasonable request. The meteorological data are available from the India Meteorological Department (IMD), Pune (www.imdpune.gov.in). The future climate model data are available in the CORDEX database.”
(Source: https://ascelibrary.org/doi/10.1061/JHYEFF.HEENG-6026)
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